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Sparse Bayesian learning in ISAR tomography imaging

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Abstract

Inverse synthetic aperture radar (ISAR) imaging can be regarded as a narrow-band version of the computer aided tomography (CT). The traditional CT imaging algorithms for ISAR, including the polar format algorithm (PFA) and the convolution back projection algorithm (CBP), usually suffer from the problem of the high sidelobe and the low resolution. The ISAR tomography image reconstruction within a sparse Bayesian framework is concerned. Firstly, the sparse ISAR tomography imaging model is established in light of the CT imaging theory. Then, by using the compressed sensing (CS) principle, a high resolution ISAR image can be achieved with limited number of pulses. Since the performance of existing CS-based ISAR imaging algorithms is sensitive to the user parameter, this makes the existing algorithms inconvenient to be used in practice. It is well known that the Bayesian formalism of recover algorithm named sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which uses an efficient expectation maximization procedure to estimate the necessary parameters, and retains a preferable property of the l 0-norm diversity measure. Motivated by that, a fully automated ISAR tomography imaging algorithm based on SBL is proposed. Experimental results based on simulated and electromagnetic (EM) data illustrate the effectiveness and the superiority of the proposed algorithm over the existing algorithms.

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Authors and Affiliations

  1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, China

    Wu-ge Su  (苏伍各), Hong-qiang Wang  (王宏强), Bin Deng  (邓彬), Rui-jun Wang  (王瑞君) & Yu-liang Qin  (秦玉亮)

Authors
  1. Wu-ge Su  (苏伍各)
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  2. Hong-qiang Wang  (王宏强)
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  3. Bin Deng  (邓彬)
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  4. Rui-jun Wang  (王瑞君)
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  5. Yu-liang Qin  (秦玉亮)
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Corresponding author

Correspondence to Wu-ge Su  (苏伍各).

Additional information

Foundation item: Project(61171133) supported by the National Natural Science Foundation of China; Project(11JJ1010) supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province, China; Project(61101182) supported by the National Natural Science Foundation for Young Scientists of China

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Su, Wg., Wang, Hq., Deng, B. et al. Sparse Bayesian learning in ISAR tomography imaging. J. Cent. South Univ. 22, 1790–1800 (2015). https://doi.org/10.1007/s11771-015-2697-1

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  • DOI: https://doi.org/10.1007/s11771-015-2697-1

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